The second direction in this section is the user-centric monitoring frameworks, in which the monitoring process is managed by the client side of the cloud environment. Emeakaroha et al. (2012) proposed CASViD, which is an architecture to monitor and detect the violations in the SLAs of cloud computing applications, through monitoring performance and usage the cloud resources according to the levels specified in the SLA document (Vincent C Emeakaroha et al., 2012). The framework is considered to be user- centric monitoring, as the SLA management is activated by client side requests. This research was extended by Brandic et al. (2015) through proposing an algorithm for determining the intervals between measurements of the applications in multi-tenancy SaaS, this was achieved by considering the cost and the SLA objectives (Brandic et al., 2015). Although violations of the SLA were detected in the two aforementioned studies, but the framework failed to define a way for notifying the client about these violations, and also to declare the web services used for transmitting the data between the client side and the provider side.
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GMonE, is a framework developed by Montes et al. (2013) for monitoring cloud environments. The authors discussed the importance of considering user-centric monitoring for cloud services, in addition to the QoS of the SLA parameters (Montes et al., 2013). Although the authors claimed that GMonE provides client-oriented monitoring for the cloud and a GUI access to the monitored data, this was considered in terms of the type of measurements and collection of the data required for the client. However, they provided no information about managing the monitoring process by the client, supporting the REST architecture, or monitoring the overall user satisfaction.
Nguyen et al. (2014) presented a user-oriented monitoring framework for cloud computing. The authors highlighted the importance of distinguishing the role of the cloud user as a consumer of cloud resources, whether it is a client or a provider in the monitoring which affects the required monitored data. They also discussed the fact that the cloud application user is more interested in receiving clear notifications about the decline of a service than the metrics details of used cloud resources. Although the researchers claimed that it is a user-centric monitor, managing the monitoring was accomplished using a trusted third party (Nguyen et al., 2014). The main limitation of this study is the failure to consider SLA compliance in monitoring cloud services.
Serhani et al. (2014) presented a study to check SLA violations in SaaS cloud computing, through measuring the QoS of the received services. The researchers discussed the importance of monitoring the SaaS services for both the cloud clients and providers, claiming that the framework can be both client- or provider-centric (Serhani et al., 2014). However, this study failed to manage an automated monitoring for the SaaS services, and there are no details about the types of web services supported. On the other hand, MonSLAR manages the monitored data for a specific REST service provider, the architecture of MonSLAR also provides more details for supporting the REST architecture in handling the monitored data.
Rehman et al. (2015) presented UCSM, a framework that assists the user in the cloud service selection process. The framework contains monitoring and early warning components. The monitoring process took into consideration collection of the QoS measurements of the cloud services and the users’ feedback about the services, to be used later by the other components (Rehman et al., 2015). Although this study considered monitoring the cloud services by considering the users’ feedback, it ignored the stated
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services’ levels in the SLA. Furthermore, this framework lacked an overall estimation for user satisfaction (QoE) based on selected parameters, and provided a poor description for technical details such as the communication mechanisms and the types of supported web services.
Moustafa et al. (2015) presented SLAM, an agent-based framework for monitoring SLA in federated cloud computing. SLAM proposed allowing the user to measure the SLA parameter through mapping it with low level metric, and uses dashboards to provide the monitored data. SLAM allowed the client to evaluate the cloud provider service. This was done by the coordinator component which sends requests to the specific provider and evaluates its performance according to the collected data. The authors claimed that this framework can be used by both the clients and the providers to monitor the cloud services (Moustafa et al., 2015). However, the research failed to provide an automated online monitoring for the cloud service, as well as consideration of the type of web services used in the cloud service, and handling of the REST architecture. This research is considered in Appendix E, through comparing the overhead caused by SLAM with that caused by MonSLAR, as it is the most relevant one to the middleware presented in this thesis. Another architecture was proposed by Tang et al. (2016) to assess trust in cloud computing based on QoS monitoring and users’ feedback. The authors presented a middleware to manage the evaluation process. In their study, trust considered as the expectation of the user about the used service. The architecture proposed providing a list of trusted services to the clients based on the middleware evaluation results and each client SLA requirements, which could help the user in selecting the most trusted service (Tang et al., 2016). Although users’ feedback was considered to evaluate the candidate services, the study did not consider measuring the individual user satisfaction of the used service. Little attention has been given to provide an automated monitoring in the proposed architecture and delivering the data to the clients.
The main limitation of the research presented in this kind of monitoring frameworks is the weakness of finding an automated monitoring environment to control SLA violations cases or giving details about the web services used in the monitoring process.